Individualized Dynamic Patient Monitoring Under Alarm Fatigue
Bibliographic record
Abstract
Individualized Patient Monitoring Under Alarm Fatigue Hospitals are rife with alarms, many of which are false. This leads to alarm fatigue, in which clinicians become desensitized and may inadvertently ignore real threats. “Individualized Dynamic Patient Monitoring Under Alarm Fatigue” by Piri, Huh, Shechter, and Hudson studies the problem of personalizing alarm thresholds for vital signs at a hospital while considering the ”boy who cried wolf” effect of false alarms. The authors create a model that learns patients’ personal alarm thresholds during their hospital stay and updates their alarm settings dynamically. They formulate the problem as a partially observable Markov decision process. They provide structural properties of the optimal policy and perform a numerical case study based on clinical data from an intensive care unit. They show that dynamic methods of alarm settings that explicitly consider the feedback loop of false positives can significantly reduce patient harm when compared with current methods of alarm settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".